Applied Spatial Statistics and Econometrics Data Analysis in R
This textbook is a comprehensive introduction to applied spatial data analysis using R. Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn. The author team showcases key topics, including unsupervised learning, causal i...
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| Hlavní autor: | |
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| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
| Vydáno: |
London
Routledge
2021
Taylor and Francis Taylor & Francis Group |
| Vydání: | 1 |
| Edice: | Routledge Advanced Texts in Economics and Finance |
| Témata: | |
| ISBN: | 0367470764, 9780367470777, 0367470772, 9780367470760 |
| On-line přístup: | Získat plný text |
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| Abstract | This textbook is a comprehensive introduction to applied spatial data analysis using R. Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn. The author team showcases key topics, including unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity and bootstrapping. It is accompanied by a suite of data and R code on Github to help readers practise techniques via replication and exercises.
This text will be a valuable resource for advanced students of econometrics, spatial planning and regional science. It will also be suitable for researchers and data scientists working with spatial data. |
|---|---|
| AbstractList | This textbook is a comprehensive introduction to applied spatial data analysis using R. Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn. The author team showcases key topics, including unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity and bootstrapping. It is accompanied by a suite of data and R code on Github to help readers practise techniques via replication and exercises.
This text will be a valuable resource for advanced students of econometrics, spatial planning and regional science. It will also be suitable for researchers and data scientists working with spatial data.
Introduction
Statement by the American Statistical Association on statistical significance and p-value used in the book
Acknowledgments
Chapter 1: Basic operations in the R software (Mateusz Kopyt) 1.1 About the R software 1.2. The R software interface 1.2.1 R Commander 1.2.2. RStudio 1.3 Using help 1.4 Additional packages 1.5 R Language - basic features 1.6 Defining and loading data 1.7 Basic operations on objects 1.8 Basic statistics of the data set 1.9 Basic visualizations 1.9.1 Scatterplot and line chart 1.9.2 Column chart 1.9.3 Pie chart 1.9.4 Boxplot 1.10 Regression in examples
Chapter 2: Spatial data, R classes and basic graphics (Katarzyna Kopczewska) 2.1 Loading and basic operations on spatial vector data 2.2. Creating, checking and converting spatial classes 2.3 Selected color palettes 2.4 Basic contour maps with a color layer Scheme 1 - with colorRampPalette() from the grDevices:: package Scheme 2 - with choropleth() from the GISTools:: package Scheme 3 - with findInterval() from the base:: package Scheme 4 - with findColours() from the classInt:: package Scheme 5 - with spplot() from the sp:: package 2.5 Basic operations and graphs for point data Scheme 1 - with points() from the graphics:: package – locations only Scheme 2 - with spplot() from the sp:: package - locations and values Scheme 3 - with findInterval() from the base:: package - locations, values, different size of symbols 2.6 Basic operations on rasters 2.7 Basic operations on grids 2.8 Spatial geometries
Chapter 3: Spatial data from the Web API (Mateusz Kopyt, Katarzyna Kopczewska) 3.1 What is the API? 3.2. Creating contextual maps with use of API 3.3 Ways to visualize spatial data - maps for point and regional data Scheme 1 - with bubbleMap() from the RgoogleMaps:: package Scheme 2 - with ggmap() from the ggmap:: package Scheme 3 - with PlotOnStaticMap() from the RgoogleMap:: package Scheme 4 - with RGoogleMaps:: GetMap() and conversion of staticMap into a raster 3.4 Spatial data in vector format - example of the OSM database 3.5 Access to non-spatial internet databases and resources via API - examples 3.6 Geo-coding of data
Chapter 4: Spatial weight matrices, distance measurement, tessellation, spatial statistics (Katarzyna Kopczewska, Maria Kubara) 4.1. Introduction to spatial data analysis 4.2 Spatial weights matrix 4.2.1 General framework for creating spatial weights matrices 4.2.2 Selection of a neighborhood matrix 4.2.3 Neighborhood matrices according to the contiguity criterion 4.2.4 Matrix of k nearest neighbors (knn) 4.2.5 Matrix based on distance criterion (neighbours in a radius of d km) 4.2.6 Inverse distance matrix 4.2.7 Summarizing and editing of spatial weights matrix 4.2.8 Spatial lags and higher order neighborhood 4.2.9 Creating weights matrix based on group membership 4.3 Distance measurement and spatial aggregation 4.4 Tessellation 4.5 Spatial statistics 4.5.1 Global statistics 4.5.1.1 Global Moran I statistics 4.5.1.2 Global Geary C statistics 4.5.1.3 Join-count statistics 4.5.2. Local spatial autocorrelation statistics 4.5.2.1 Local Moran I statistics (LISA) 4.5.2.2 Local Geary C statistics 4.5.2.3 Local Getis-Ord Gi statistics 4.5.2.4. Local spatial heteroscedasticity (LOSH) 4.6 Spatial cross-correlations for two variables 4.7 Correlogram
Chapter 5: Applied spatial econometrics (Katarzyna Kopczewska) 5.1 Value added from spatial modelling and classes of models 5.2 Basic cross-sectional models 5.2.1 Estimation 5.2.2 Quality assessment of spatial models 5.2.2.1 Information criteria and pseudo R2 in assessing model fit 5.2.2.2 Test for heteroskedasticity of model residuals 5.2.2.3 Residual autocorrelation tests 5.2.2.4 LM tests for model type selection 5.2.2.5 LR and Wald tests for model restrictions 5.2.3 Selection of spatial weight matrix and modelling of diffusion strength 5.2.4 Forecasts in spatial models 5.2.5 Causality 5.3 Selected specifications of cross-sectional spatial models 5.3.1 Uni-directional spatial interaction models 5.3.2 Cumulative models 5.3.3 Bootstrapped models for big data 5.3.4 Models for grid data 5.4 Spatial panel models
Chapter 6: Geographically Weighted Regression - modelling spatial heterogeneity (Piotr Ćwiakowski) 6.1 Geographically weighted regression 6.2 Basic estimation of GWR model 6.2.1 Estimation of the reference OLS model 6.2.2 Choosing the optimal bandwidth for a dataset 6.2.3 Local geographically weighted statistics 6.2.4 Geographically weighted regression estimation 6.2.5 Basic diagnostic tests of the GWR model 6.2.6 Testing the significance of parameters in GWR 6.2.7 Selection of the optimal functional form of the model 6.2.8 GWR with heteroskedastic random error 6.3 The problem of collinearity in GWR models 6.3.1 Diagnosing collinearity in GWR 6.4. Mixed GWR 6.5. Robust regression in the GWR model 6.6. Geographically and Temporally Weighted Regression (GTWR)
Chapter 7: Unattended spatial learning (Katarzyna Kopczewska) 7.1 Clustering of spatial points with k-means, PAM and CLARA algorithms 7.2 Clustering with the DBSCAN algorithm 7.3 Spatial Principal Component Analysis 7.4 Spatial Drift 7.5 Spatial hierarchical clustering 7.6 Spatial oblique decision tree
Chapter 8: Spatial point pattern analysis and spatial interpolation (Kateryna Zabarina) 8.1. Introduction and main definitions 8.1.1. Dataset 8.1.2. Creation of window and point pattern 8.1.3. Marks 8.1.4. Covariates 8.1.5. Duplicated points 8.1.6. Projection and rescaling 8.2. Intensity-based analysis of unmarked point pattern 8.2.1. Quadrat test 8.2.2. Tests with spatial covariates 8.3. Distance-based analysis of the unmarked point pattern 8.3.1. Distance-based measures 8.3.1.1. Ripley’s K function 8.3.1.2. F function 8.3.1.3. G function 8.3.1.4. J function 8.3.1.5. Distance-based CSR tests 8.3.2. Monte-Carlo tests 8.3.3. Envelopes 8.3.4. Non-graphical tests 8.4. Selection and estimation of a proper model for unmarked point pattern 8.4.1. Theoretical note 8.4.2. Choice of parameters 8.4.3. Estimation and results 8.4.4. Conclusions 8.5. Intensity-based analysis of marked point pattern 8.5.1. Segregation test 8.6. Correlation and spacing analysis of the marked point pattern 8.6.1. Analysis under assumption of stationarity 8.6.1.1. K function variations for multitype pattern 8.6.1.2. Mark connection function 8.6.1.3. Analysis of within and between types of dependence 8.6.1.4. Randomisation test of components’ independence 8.6.2. Analysis under assumption of non-stationarity 8.6.2.1. Inhomogeneous K function variations for multitype pattern 8.7. Selection and estimation of a proper model for unmarked point pattern 8.7.1. Theoretical note 8.7.2. Choice of optimal radius 8.7.3. Within-industry interaction radius 8.7.4. Between-industry interaction radius 8.7.5. Estimation and results 8.7.6. Model with no between-industry interaction 8.7.7. Model with all possible interactions 8.8. Spatial interpolation methods - kriging 8.8.1. Basic definitions 8.8.2. Description of chosen kriging methods 8.8.3. Data preparation for the study 8.8.4. Estimation and discussion
Chapter 9: Spatial Sampling and Bootstrap (Katarzyna Kopczewska, Piotr Ćwiakowski) 9.1 Spatial point data - object classes and spatial aggregation 9.2 Spatial sampling - randomization / generation of new points on the surface 9.3 Spatial sampling - sampling of sub-samples from existing points 9.3.1 Simple sampling 9.3.2 The options of the sperrorest:: package 9.3.3 Sampling points from areas determined by the k-means algorithm - block bootstrap 9.3.4 Sampling points from moving blocks (moving block bootstrap, MBB) 9.4. The use of spatial sampling and bootstrap in cross-validation of models
Chapter 10: Spatial Big Data (Piotr Wójcik) 10.1. Examples of big data usage 10.2. Spatial big data 10.2.1. Spatial data types 10.2.2. Challenges related to the use of spatial Big Data 10.2.2.1. Processing of large data sets 10.2.2.2. Mapping and reduction 10.2.2.3. Spatial data indexing 10.3. The sf:: package - simple features 10.3.1 sf class – a special data frame 10.3.2 Data with POLYGON geometry 10.3.3 Data with POINT geometry 10.3.4 Visualization using the ggplot2:: package 10.3.5 Selected functions for spatial analysis 10.4. Using the dplyr:: package functions 10.5. Example analysis of large raster data 10.5.1. Measurement of economic inequalities from space 10.5.2. Analysis using the raster:: package functions 10.5.3 Other functions of the raster:: package 10.5.4 Potential alternative – stars:: package
Chapter 11: Spatial unsupervised learning – applications of market basket analysis in geomarketing (Alessandro Festi) 11.1 Introduction to market basket analysis 11.2 Data needed in spatial market basket analysis 11.3 Simulation of data 11.4 The market basket analysis technique applied to geolocation data 11.5 Spatial association rules 11.6 Applications to geomarketing 11.6.1 Finding the best location for a business 11.6.2 Targeting 11.6.3 Discovery of competitors 11.7 Conclusions and further approaches
Appendix 1: Data used in the examples A1. Data set No. 1 / dataset1 / - poviat panel data with many variables A2. Dataset no 2 /dataset2/ – geo-located point data A3. Dataset no 3 /dataset3/ – monthly unemployment rate in poviats (NTS4) A4. Dataset no 4 /dataset4/ - grid data f This textbook is a comprehensive introduction to applied spatial data analysis using R. Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn. The author team showcases key topics, including unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity and bootstrapping. It is accompanied by a suite of data and R code on Github to help readers practise techniques via replication and exercises. This text will be a valuable resource for advanced students of econometrics, spatial planning and regional science. It will also be suitable for researchers and data scientists working with spatial data. This textbook is a comprehensive introduction to applied spatial data analysis using R. Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn. The author team showcases key topics, including unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity and bootstrapping. It is accompanied by a suite of data and R code on Github to help readers practise techniques via replication and exercises. This text will be a valuable resource for advanced students of econometrics, spatial planning and regional science. It will also be suitable for researchers and data scientists working with spatial data. This textbook is a comprehensive introduction to applied spatial data analysis, using R. Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn. |
| Author | Kopczewska, Katarzyna |
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| Copyright | 2021 Katarzyna Kopczewska |
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| Keywords | Spatial PCA Spatial Lag FALSE FALSE NA NA NA GWR Model GWR Coefficient Inverse Distance Matrix MBB OLS Estimate BP Test SDM Model Point Pattern Marked Point Pattern ESRI Shapefile NA NA NA NA Spatial Weights Spatial Weights Matrix GWR Min 1Q Median 3Q Max FALSE FALSE FALSE FALSE FALSE NA NA Spatial Weights Matrices NA NA NA NA NA Market Basket Analysis FALSE FALSE FALSE |
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| Notes | Includes bibliographical references (p. 561-576) and index |
| OCLC | 1202475596 |
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| PublicationPlace | London |
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| PublicationSeriesTitle | Routledge Advanced Texts in Economics and Finance |
| PublicationYear | 2021 2020 |
| Publisher | Routledge Taylor and Francis Taylor & Francis Group |
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| Snippet | This textbook is a comprehensive introduction to applied spatial data analysis using R. Each chapter walks the reader through a different method, explaining... This textbook is a comprehensive introduction to applied spatial data analysis, using R. Each chapter walks the reader through a different method, explaining... |
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| SubjectTerms | Econometrics Economic Geography R (Computer program language) Spatial analysis (Statistics) Spatial and Regional Planning |
| Subtitle | Data Analysis in R |
| TableOfContents | 8.6.1.2 Mark connection function -- 8.6.1.3 Analysis of within- and between-type dependence -- 8.6.1.4 Randomisation test of components' independence -- 8.6.2 Analysis under assumption of non-stationarity -- 8.6.2.1 Inhomogeneous K function variations for multitype pattern -- 8.7 Selection and estimation of a proper model for unmarked point pattern -- 8.7.1 Theoretical note -- 8.7.2 Choice of optimal radius -- 8.7.3 Within-industry interaction radius -- 8.7.4 Between-industry interaction radius -- 8.7.5 Estimation and results -- 8.7.6 Model with no between-industry interaction -- 8.7.7 Model with all possible interactions -- 8.8 Spatial interpolation methods - kriging -- 8.8.1 Basic definitions -- 8.8.2 Description of chosen kriging methods -- 8.8.3 Data preparation for the study -- 8.8.4 Estimation and discussion -- 9 Spatial sampling and bootstrapping -- 9.1 Spatial point data - object classes and spatial aggregation -- 9.2 Spatial sampling - randomisation/generation of new points on the surface -- 9.3 Spatial sampling - sampling of sub-samples from existing points -- 9.3.1 Simple sampling -- 9.3.2 The options of the sperrorest:: package -- 9.3.3 Sampling points from areas determined by the k-means algorithm - block bootstrap -- 9.3.4 Sampling points from moving blocks (moving block bootstrap) -- 9.4 Use of spatial sampling and bootstrapping in cross-validation of models -- ### Example ### -- 10 Spatial big data -- 10.1 Examples of big data applications -- 10.2 Spatial big data -- 10.2.1 Spatial data types -- 10.2.2 Challenges related to the use of spatial big data -- 10.2.2.1 Processing of large datasets -- 10.2.2.2 Mapping and reduction -- 10.2.2.3 Spatial data indexing -- 10.3 The sd:: package - simple features -- 10.3.1 sf class - a special data frame -- 10.3.2 Data with POLYGON geometry -- 10.3.3 Data with POINT geometry 10.3.4 Visualisation using the ggplot2:: package Cover -- Half Title -- Series -- Title -- Copyright -- Contents -- List of figures -- List of tables -- List of contributors -- Introduction -- Statement by the American Statistical Association on statistical significance and p-value - use in the book -- Acknowledgements -- 1 Basic operations in the R software -- 1.1 About the R software -- 1.2 The R software interface -- 1.2.1 R Commander -- 1.2.2 RStudio -- 1.3 Using help -- 1.4 Additional packages -- 1.5 R language - basic features -- 1.6 Defining and loading data -- 1.7 Basic operations on objects -- 1.8 Basic statistics of the dataset -- 1.9 Basic visualisations -- 1.9.1 Scatterplot and line chart -- 1.9.2 Column chart -- 1.9.3 Pie chart -- 1.9.4 Boxplot -- 1.10 Regression in examples -- 2 Data, spatial classes and basic graphics -- 2.1 Loading and basic operations on spatial vector data -- 2.2 Creating, checking and converting spatial classes -- 2.3 Selected colour palettes -- 2.4 Basic contour maps with a colour layer -- Scheme 1 - with colorRampPalette() from the grDevices:: package -- Scheme 2 - with choropleth() from the GISTools:: package -- Scheme 3 - with findInterval() from the base:: package -- Scheme 4 - with findColours() from the classInt:: package -- Scheme 5 - with spplot() from the sp:: package -- 2.5 Basic operations and graphs for point data -- Scheme 1 - with points() from the graphics:: package - locations only -- Scheme 2 - with spplot() from the sp:: package - locations and values -- Scheme 3 - with findInterval() from the base:: package - locations, values, different size of symbols -- 2.6 Basic operations on rasters -- 2.7 Basic operations on grids -- 2.8 Spatial geometries -- 3 Spatial data with Web APIs -- 3.1 What is an application programming interface (API)? -- 3.2 Creating background maps with use of an application programming interface 6.5 Robust regression in the geographically weighted regression model -- 6.6 Geographically and temporally weighted regression -- 7 Spatial unsupervised learning -- 7.1 Clustering of spatial points with k-means, PAM (partitioning around medoids) and CLARA (clustering large applications) algorithms -- ### Example ### -- ### Example ### -- 7.2 Clustering with the density-based spatial clustering of applications with noise algorithm -- ### Example ### -- 7.3 Spatial principal component analysis -- ### Example ### -- 7.4 Spatial drift -- ### Example ### -- 7.5 Spatial hierarchical clustering -- ### Example ### -- ### Example ### -- 7.6 Spatial oblique decision tree -- ### Example ### -- 8 Spatial point pattern analysis and spatial interpolation -- 8.1 Introduction and main definitions -- 8.1.1 Dataset -- 8.1.2 Creation of window and point pattern -- 8.1.3 Marks -- 8.1.4 Covariates -- ### Example ### -- 8.1.5 Duplicated points -- 8.1.6 Projection and rescaling -- 8.2 Intensity-based analysis of unmarked point pattern -- 8.2.1 Quadrat test -- 8.2.2 Tests with spatial covariates -- 8.3 Distance-based analysis of the unmarked point pattern -- 8.3.1 Distance-based measures -- 8.3.1.1 Ripley's K function -- 8.3.1.2 F function -- 8.3.1.3 G function -- 8.3.1.4 J function -- 8.3.1.5 Distance-based complete spatial randomness tests -- 8.3.2 Monte Carlo tests -- 8.3.3 Envelopes -- 8.3.4 Non-graphical tests -- 8.4 Selection and estimation of a proper model for unmarked point pattern -- 8.4.1 Theoretical note -- 8.4.2 Choice of parameters -- 8.4.3 Estimation and results -- 8.4.4 Conclusions -- 8.5 Intensity-based analysis of marked point pattern -- 8.5.1 Segregation test -- 8.6 Correlation and spacing analysis of the marked point pattern -- 8.6.1 Analysis under assumption of stationarity -- 8.6.1.1 K function variations for multitype pattern 5.1 Added value from spatial modelling and classes of models -- 5.2 Basic cross-sectional models -- 5.2.1 Estimation -- ### Example ### -- 5.2.2 Quality assessment of spatial models -- 5.2.2.1 Information criteria and pseudo-R2 in assessing model fit -- 5.2.2.2 Test for heteroscedasticity of model residuals -- 5.2.2.3 Residual autocorrelation tests -- 5.2.2.4 Lagrange multiplier tests for model type selection -- 5.2.2.5 Likelihood ratio and Wald tests for model restrictions -- 5.2.3 Selection of spatial weights matrix and modelling of diffusion strength -- 5.2.4 Forecasts in spatial models -- 5.2.5 Causality -- 5.3 Selected specifications of cross-sectional spatial models -- 5.3.1 Unidirectional spatial interaction models -- 5.3.2 Cumulative models -- 5.3.3 Bootstrapped models for big data -- ### Example ### -- 5.3.4 Models for grid data -- ### Example ### -- 5.4 Spatial panel models -- ### Example### -- 6 Geographically weighted regression - modelling spatial heterogeneity -- 6.1 Geographically weighted regression -- 6.2 Basic estimation of geographically weighted regression model -- 6.2.1 Estimation of the reference ordinary least squares model -- 6.2.2 Choosing the optimal bandwidth for a dataset -- 6.2.3 Local geographically weighted statistics -- 6.2.4 Geographically weighted regression estimation -- 6.2.5 Basic diagnostic tests of the geographically weighted regression model -- 6.2.6 Testing the significance of parameters in geographically weighted regression -- 6.2.7 Selection of the optimal functional form of the model -- 6.2.8 Geographically weighted regression with heteroscedastic random error -- 6.3 The problem of collinearity in geographically weighted regression models -- 6.3.1 Diagnosing collinearity in geographically weighted regression -- 6.4 Mixed geographically weighted regression 3.3 Ways to visualise spatial data - maps for point and regional data -- Scheme 1 - with bubbleMap() from the RgoogleMaps:: package -- Scheme 2 - with ggmap() from the ggmap:: package -- Scheme 3 - with PlotOnStaticMap() from the RgoogleMaps:: package -- Scheme 4 - with RGoogleMaps:: GetMap() and conversion of staticMap into a raster -- 3.4 Spatial data in vector format - example of the OSM database -- 3.5 Access to non-spatial internet databases and resources via application programming interface - examples -- 3.6 Geocoding of data -- 4 Spatial weights matrix, distance measurement, tessellation, spatial statistics -- 4.1 Introduction to spatial data analysis -- 4.2 Spatial weights matrix -- 4.2.1 General framework for creating spatial weights matrices -- 4.2.2 Selection of a neighbourhood matrix -- 4.2.3 Neighbourhood matrices according to the contiguity criterion -- 4.2.4 Matrix of k nearest neighbours (knn) -- 4.2.5 Matrix based on distance criterion (neighbours in a radius of d km) -- 4.2.6 Inverse distance matrix -- 4.2.7 Summarising and editing spatial weights matrix -- 4.2.8 Spatial lags and higher-order neighbourhoods -- 4.2.9 Creating weights matrix based on group membership -- ### Example ### -- ### Example ### -- 4.3 Distance measurement and spatial aggregation -- ### Example ### -- 4.4 Tessellation -- 4.5 Spatial statistics -- 4.5.1 Global statistics -- 4.5.1.1 Global Moran's I statistics -- 4.5.1.2 Global Geary's C statistics -- 4.5.1.3 Join-count statistics -- 4.5.2 Local spatial autocorrelation statistics -- 4.5.2.2 Local Moran's I statistics (local indicator of spatial association) -- 4.5.2.3 Local Geary's C statistics -- 4.5.2.4 Local Getis-Ord Gi statistics -- 4.5.2.5 Local spatial heteroscedasticity -- 4.6 Spatial cross-correlations for two variables -- 4.7 Correlogram -- 5 Applied spatial econometrics |
| Title | Applied Spatial Statistics and Econometrics |
| URI | https://www.taylorfrancis.com/books/9781003033219 https://www.taylorfrancis.com/books/9781000079784 https://cir.nii.ac.jp/crid/1130287121408403977 https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=6357314 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9781000079746 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9781000079784 |
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